Package 'sreg'

Replication data for: Iron Deficiency and Schooling Attainment in Peru (Chong et al, 2016)

Description

The data is taken from Chong et al. (2016), who study the effect of iron deficiency anemia (i.e., anemia caused by a lack of iron) on school-age children’s educational attainment and cognitive ability in Peru.

Usage

data("AEJapp")

Format

A data frame with 215 observations on the 62 variables.

Source

Chong, A., Cohen, I., Field, E., Nakasone, E., and Torero, M. (2016). Replication data for: Iron Deficiency and Schooling Attainment in Peru. Nashville, TN: American Economic Association [publisher], 2016. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-10-12. doi:10.3886/E113624V1.

References

Chong, A., Cohen, I., Field, E., Nakasone, E., and Torero, M. (2016). Iron Deficiency and Schooling Attainment in Peru. American Economic Journal: Applied Economics, 8(4), 222–255. doi:10.1257/app.20140494.

Examples

data(AEJapp)

---

Print sreg Objects

Description

Print the summary table of estimation results for sreg objects.

Usage

## S3 method for class 'sreg'
print(x, ...)

Arguments

x	An object of class sreg.
...	Additional arguments passed to other methods.

Value

No return value, called for side effects.

Examples

data <- sreg.rgen(n = 200, tau.vec = c(0.1), n.strata = 4, cluster = TRUE)
Y <- data$Y
S <- data$S
D <- data$D
X <- data.frame("x_1" = data$x_1, "x_2" = data$x_2)
result <- sreg(Y, S, D, G.id = NULL, Ng = NULL, X)
print(result)

---

Estimate Average Treatment Effects (ATEs) and Corresponding Standard Errors

Description

Estimate the ATE(s) and the corresponding standard error(s) for a (collection of) treatment(s) relative to a control.

Usage

sreg(Y, S = NULL, D, G.id = NULL, Ng = NULL, X = NULL, HC1 = TRUE)

Arguments

Y	a numeric $n \times 1$ vector/matrix/data.frame/tibble of the observed outcomes
S	a numeric $n \times 1$ vector/matrix/data.frame/tibble of strata indicators indexed by $\{1, 2, 3, \ldots\}$; if NULL then the estimation is performed assuming no stratification
D	a numeric $n \times 1$ vector/matrix/data.frame/tibble of treatments indexed by $\{0, 1, 2, \ldots\}$, where $\code{D} = 0$ denotes the control
G.id	a numeric $n \times 1$ vector/matrix/data.frame/tibble of cluster indicators; if NULL then estimation is performed assuming treatment is assigned at the individual level
Ng	a numeric $n \times 1$ vector/matrix/data.frame/tibble of cluster sizes; if NULL then Ng is assumed to be equal to the number of available observations in every cluster
X	a matrix/data.frame/tibble with columns representing the covariate values for every observation; if NULL then the estimator without linear adjustments is applied. (Note: sreg cannot use individual-level covariates for covariate adjustment in cluster-randomized experiments. Any individual-level covariates will be aggregated to their cluster-level averages)
HC1	a TRUE/FALSE logical argument indicating whether the small sample correction should be applied to the variance estimator

Value

An object of class sreg that is a list containing the following elements:

• tau.hat: a $1 \times |\mathcal A|$ vector of ATE estimates, where $|\mathcal A|$ represents the number of treatments

• se.rob: a $1 \times |\mathcal A|$ vector of standard errors estimates, where $|\mathcal A|$ represents the number of treatments

• t.stat: a $1 \times |\mathcal A|$ vector of $t$-statistics, where $|\mathcal A|$ represents the number of treatments

• p.value: a $1 \times |\mathcal A|$ vector of corresponding $p$-values, where $|\mathcal A|$ represents the number of treatments

• CI.left: a $1 \times |\mathcal A|$ vector of the left bounds of the 95% as. confidence interval

• CI.right: a $1 \times |\mathcal A|$ vector of the right bounds of the 95% as. confidence interval

• data: an original data of the form data.frame(Y, S, D, G.id, Ng, X)

• lin.adj: a data.frame representing the covariates that were used in implementing linear adjustments

Author(s)

Authors:

Juri Trifonov [email protected]

Yuehao Bai [email protected]

Azeem Shaikh [email protected]

Max Tabord-Meehan [email protected]

Maintainer:

Juri Trifonov [email protected]

References

Bugni, F. A., Canay, I. A., and Shaikh, A. M. (2018). Inference Under Covariate-Adaptive Randomization. Journal of the American Statistical Association, 113(524), 1784–1796, doi:10.1080/01621459.2017.1375934.

Bugni, F., Canay, I., Shaikh, A., and Tabord-Meehan, M. (2024+). Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes. Forthcoming in the Journal of Political Economy: Microeconomics, doi:10.48550/arXiv.2204.08356.

Jiang, L., Linton, O. B., Tang, H., and Zhang, Y. (2023+). Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance. Forthcoming in Review of Economics and Statistics, doi:10.48550/arXiv.2204.08356.

Examples

library("sreg")
library("dplyr")
library("haven")
### Example 1. Simulated Data.
data <- sreg.rgen(n = 1000, tau.vec = c(0), n.strata = 4, cluster = FALSE)
Y <- data$Y
S <- data$S
D <- data$D
X <- data.frame("x_1" = data$x_1, "x_2" = data$x_2)
result <- sreg(Y, S, D, G.id = NULL, Ng = NULL, X)
print(result)
### Example 2. Empirical Data.
?AEJapp
data("AEJapp")
data <- AEJapp
head(data)
Y <- data$gradesq34
D <- data$treatment
S <- data$class_level
data.clean <- data.frame(Y, D, S)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
table(D = data.clean$D, S = data.clean$S)
result <- sreg(Y, S, D)
print(result)
pills <- data$pills_taken
age <- data$age_months
data.clean <- data.frame(Y, D, S, pills, age)
data.clean <- data.clean %>%
  mutate(D = ifelse(D == 3, 0, D))
Y <- data.clean$Y
D <- data.clean$D
S <- data.clean$S
X <- data.frame("pills" = data.clean$pills, "age" = data.clean$age)
result <- sreg(Y, S, D, G.id = NULL, X = X)
print(result)

---

Generate a Pseudo-Random Sample under the Stratified Block Randomization Design

Description

The function generates the observed outcomes, treatment assignments, strata indicators, cluster indicators, cluster sizes, and covariates for estimating the treatment effect within the context of a stratified block randomization design under the covariate-adaptive randomization (CAR).

Usage

sreg.rgen(
  n,
  Nmax = 50,
  n.strata,
  tau.vec = c(0),
  gamma.vec = c(0.4, 0.2, 1),
  cluster = TRUE,
  is.cov = TRUE,
  pi.vec = NULL
)

Arguments

n	a total number of observations in a sample
Nmax	a maximum size of generated clusters (maximum number of observations in a cluster)
n.strata	an integer specifying the number of strata
tau.vec	a numeric $1 \times |\mathcal A|$ vector of treatment effects, where $|\mathcal A|$ represents the number of treatments
gamma.vec	a numeric $1 \times 3$ vector of parameters corresponding to covariates
cluster	a TRUE/FALSE argument indicating whether the dgp should use a cluster-level treatment assignment or individual-level
is.cov	a TRUE/FALSE argument indicating whether the dgp should include covariates or not

Value

An object that is a 'data.frame' with $n$ observations containing the generated values of the following variables:

• Y: a numeric $n \times 1$ vector of observed outcomes

• S: a numeric $n \times 1$ vector of strata indicators

• D: a numeric $n \times 1$ vector of treatments indexed by $\{0, 1, 2, \ldots\}$, where $\code{D} = 0$ denotes the control

• G.id: a numeric $n \times 1$ vector of cluster indicators

• X: a data.frame with columns representing the covariate values for every observation

Examples

data <- sreg.rgen(n = 1000, tau.vec = c(0), n.strata = 4, cluster = TRUE)

---